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% Options for packages loaded elsewhere
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pdftitle={Supplemental information for neonSoilFlux: An R Package for Continuous Sensor-Based Estimation of Soil CO2 Fluxes},
colorlinks=true,
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\title{Supplemental information for \texttt{neonSoilFlux}: An R Package
for Continuous Sensor-Based Estimation of Soil CO\textsubscript{2}
Fluxes}
\author{}
\date{}
\begin{document}
\maketitle
\section{Assessment of data gaps}\label{assessment-of-data-gaps}
For a given half-hourly time period, the \texttt{neonSoilFlux} packages
assigns a QA flag for a measurement if more one values across all
measurement depths uses gap-filled data (Section 4.2.1 of the main
text). Panel a of Figure~\ref{fig-gap-filled-stats} reports the
proportion of gap-filled data for all input environmental measurements
at each site during the period when field measurements were made. Soil
fluxes are computed from 4 different types of input measurements
(\(T_{S}\), \(SWC\), \(P\), and CO\(_{2}\)), any of which could have a
QA flag in a half-hourly interval. Panel b of
Figure~\ref{fig-gap-filled-stats} displays at each site the distribution
of the number of different gap-filled measurements used to compute a
half-hourly flux. The largest cause of measurements needing to be
gap-filled was missing or flagged soil moisture data. Calculating fluxes
for WOOD, WREF, and SJER required using the largest proportion of
gap-filled measurements, due to flagged or missing \(SWC\) and
CO\(_{2}\) data.
\begin{figure}
\centering{
\pandocbounded{\includegraphics[keepaspectratio]{figures/gap-filled-stats.png}}
}
\caption{\label{fig-gap-filled-stats}Panel a) Proportion of input
gap-filled environmental measurements used to generate \(F_{S}\) from
the \texttt{neonSoilFlux} package, by study site. Panel b) distribution
of the usage of gap-filled measurements at each site.}
\end{figure}%
\section{Assessing the signal to noise ratio (SNR) and evaluating
estimated
uncertainties}\label{assessing-the-signal-to-noise-ratio-snr-and-evaluating-estimated-uncertainties}
Following collection of field measurements and calculation of the soil
fluxes from \texttt{neonSoilFlux} package, we compared measured
\(F_{S}\) based on closed-dynamic chamber measurements with the LI-COR
instruments to a given soil flux calculation from \texttt{neonSoilFlux}
for each site and flux computation method. Beyond the model statistics
defined in the main text, we computed the signal to noise ratio (SNR),
defined as the ratio of a modeled soil flux (\(F_{ijk}\)) from
\texttt{neonSoilFlux} to its quadrature uncertainty (\(\sigma_{ijk}\)).
We observed that the range of values (\emph{e.g.}
\(F_{ijk} \pm \sigma_{ijk}\) was much larger than the measured field
flux. We evaluated \(| F_{S} - F_{ijk} | < (1-\epsilon) \sigma_{ijk}\),
where \(F_{S}\) is a measured field soil flux from the LI-COR 6800 (as
the LI-COR 870/8250 was used at only three sites in 2024 but the 6800
was used at all sites in both years). The parameter \(\epsilon\) was an
uncertainty reduction factor to evaluate how much the quadrature
uncertainty could be reduced while maintaining precision between modeled
\(F_{ijk}\) and measured \(F_{S}\).
The computed signal to noise ratio (SNR) and the proportion of measured
field fluxes within the modeled uncertainty for a given flux computation
method \(F_{ijk}\) suggest that there was substantial variability in the
agreement between the gradient method and field-measured observations
(Figure~\ref{fig-uncertainty-stats}). Here, values of SNR greater than
unity (vertical dashed lines in Figure~\ref{fig-uncertainty-stats})
indicate lower reported uncertainty, as propagated by quadrature due to
a relatively higher precision of measured input variables (CO\(_{2}\),
\(T_{S}\), \(SWC\), or \(P\)).
The sensitivity to an uncertainty reduction factor (\(\epsilon\), bottom
panels in Figure~\ref{fig-uncertainty-stats}) demonstrates how
concordance between measured and modeled fluxes would be affected if
environmental measurement uncertainty \(\sigma_{ijk}\) were to decrease.
As \(\epsilon\) increases from left to right in each figure, the
possible range of values for each predicted flux value decreases and the
proportion of measured fluxes that fall within that range also
decreases.
\begin{figure}
\centering{
\pandocbounded{\includegraphics[keepaspectratio]{figures/uncertainty-stats.png}}
}
\caption{\label{fig-uncertainty-stats}Top panels: distribution of SNR
values across each of the different sites for modeled effluxes from the
\texttt{neonSoilFlux} package, depending on the diffusivity calculation
used (Millington-Quirk or Marshall, Section 3.2.2 of the main text).
Dashed lines indicate a signal to noise ratio of 1. Bottom panels:
Proportion of measured \(F_{S}\) within the modeled range of a flux
computation method \(F_{ijk}\) given an uncertainty reduction factor
\(\epsilon\), or \(| F_{S} - F_{ijk} | < (1-\epsilon) \sigma_{ijk}\).}
\end{figure}%
\end{document}